MSTR-Robotics-magerdaniel 0.5.11

Automation toolkit for MicroStrategy / Strategy One: object read-out, migration, comparison, RAG and OSI export.

MSTR – Robotics

AI on top of BI — a Python automation toolkit for MicroStrategy / Strategy One.

Object read-out, migration, environment comparison, regression testing, RAG cubes, OSI dashboard export, and a custom MCP server that lets an AI assistant drive your BI platform directly.

Maturity: beta, sometimes alpha 🙂 Everything here is built and tested against MicroStrategy Tutorial and Platform Analytics. For real use cases you will want to adjust the notebooks — or skip them and call the libraries yourself.

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Goals of this package

• Get you running against Strategy Tutorial with as little setup as possible
• Share experience in coding on top of MicroStrategy
• Share the code itself

Delivered for free, in two parts:

1. The Python package (this repository)
2. The MSTR Object Manager packages for Tutorial & Platform Analytics, in OM_packages/

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What's in it

Custom MCP server

An MCP server is the bridge that connects an AI assistant to your own programs — here, to Python code that talks to MicroStrategy. With a custom MCP server you can automate individual workflows in natural language.

mstr_robotics/mcp_servers/mstr_osi_mcp.py registers eight tools:

Tool	Purpose
find_dashboard_for_question	Find an existing report/dashboard that can answer a business question
resolve_object_by_path	Turn a breadcrumb path into an object ID
get_object_definitions	Fetch full object definitions as JSON
query_bi_report	Answer report-wizard prompts from a natural-language question
export_report_tabular	Export a report to tabular data
get_visualization_data	Read the live data behind a single visualization
run_and_answer_bi_question	Run a BI question and enrich/expand the result
query_wikidata_sparql	Enrich BI data with external Wikidata properties

Things this makes possible:

• Compare two dashboards. Point the AI at two screenshots; it extracts the breadcrumb paths, resolves both IDs, pulls both JSON definitions and diffs them — down to "the Cost column is bound to a different metric in each dashboard". Worth knowing: developers can rename objects, so names alone don't prove equality.
• Find the dashboard you need instead of building a new one.
• Answer report-wizard prompts in natural language, then verify the generated report inside Strategy itself.
• Enrich BI data with external sources and build something on top of it.

Requires an MCP-capable AI client (e.g. Claude). Some tools additionally use a Perplexity API key — see Configuration.

Jupyter notebooks

Notebooks are Command Manager 2.0. Behind a JupyterHub they give MSTR developers a stable, safe entry point for metadata analysis. All notebooks live in notebooks/:

Notebook	What it does
00_setup	Run first. Validates config and resolves your project paths
jup_load_rag_cubes	Run second. Builds the RAG cubes the other tooling reads
jup_prj_obj_exporter	Exports metadata to Redis for analysis
jup_migrate	Migration driven by an Excel package list
jup_REGAM	Regression testing from Platform Analytics
jup_chat_answer_prompt_page	Report-wizard prompt answering, notebook edition
jup_schema_monitor	Schema monitoring
jup_osi_file_generator	Exports dashboards to OSI YAML
jup_mstr_admin	Administrative helpers

Migrations

Define your migration package from the Platform Analytics Change Logs dashboard and export it to Excel. jup_migrate then drives the automated deployment from that spreadsheet — optionally staging packages through Azure Blob storage.

Regression testing (REGAM)

Platform Analytics as the source of truth for what your users actually do: execution times, result row counts, involved views / tables / columns, all jobs from last Monday, top x reports per user group, security groups. From that, mstr_robotics simulates user behaviour for regression tests — aiming at meaningful coverage at an acceptable system workload.

• Test definition — pick the workload from PA
• Test preparation — extract, parse and enrich the raw data. PA was never designed for this, and the metadata changes constantly, so exception handling has to be forgiving.
• Test execution — in the current version, mstr_robotics creates report copies with stored prompt answers. To analyse results you can keep static copies of prompted reports for Integrity Manager, or compare execution times from EM/PA and database logs.

Metadata export to Redis

Reading metadata through the REST APIs means restricted access, poor query performance, and several APIs to stitch together. Exporting it into Redis once turns it into something you can actually query — a powerful way to monitor and clean your metadata.

You need a Redis DB — on prem or cloud. The free/open version is fine: install it locally, or take it as a managed service (e.g. StackIT).

RAG cubes

Same idea as the Redis export, but the data lands where both AI and humans can read it. RAG cubes provide metadata as context to an AI, and stay verifiable by people because they are ordinary cubes in your BI platform. Technically: read the JSON definitions, transform them to tabular, load them into cubes.

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Installation

Minimum requirements

• A supported MicroStrategy / Strategy One version, on prem or in cloud
• Architect privileges
• Python 3.12+ (developed on 3.12.7)
• A Python IDE

Strongly recommended: Visual Studio Code, and the possibility of vibe-coding — if something here doesn't fit your enterprise standard, you can fix it by writing (or vibing) code.

1. Set up a Python environment

python -m venv .venv
.venv\Scripts\Activate.ps1        # Windows;  source .venv/bin/activate on Unix

2. Install the package

From PyPI:

pip install "MSTR-Robotics-magerdaniel[all,dev]"

Or from a clone, for editable development:

git clone https://github.com/magerdaniel/MSTR_Robotics.git
cd MSTR_Robotics
pip install -e ".[all,dev]"

The base install is deliberately small — connection, object read-out, comparison, migration and OSI export. Everything feature-specific is an opt-in extra, and plain pip install . installs none of them:

Extra	Adds	Needed for
[rag]	openai, flashtext	chat / RAG notebooks
[redis]	redis	metadata export to Redis
[azure]	azure-storage-blob	Azure Blob staging in migrations
[servers]	mcp (+ implies rag)	the MCP servers
[all]	all four of the above	everything
[dev]	ruff, vulture, jupyter	linting and a Jupyter kernel

Pick any combination:

pip install -e ".[rag,redis,servers]"      # no Azure
pip install -e ".[redis,azure]"            # no AI stack at all
pip install -e ".[rag,redis,azure,dev]"    # everything but the MCP servers
pip install -e ".[all,dev]"                # the full dev machine

Note that [all] does not include [dev], so [all] alone leaves you without a Jupyter kernel. Quoting the brackets is required in PowerShell and zsh.

3. Install Jupyter

Covered by [dev] above. Otherwise: pip install jupyter.

4. Copy the utils folders into your project

A non-editable install bundles all the reference material under .venv\Lib\site-packages\mstr_robotics\utils\. Copy those folders up into your own Python project folder so the notebooks and config sit next to your work:

config/          notebooks/       examples/        OM_packages/
docs/            OSI_production/  osi_templates/

If you installed with -e from a clone, they are already at project level — skip this step.

5. Deploy the Object Manager packages

The notebooks and libraries read from — and write into — cubes, reports and dossiers that must exist in your MicroStrategy environment. Everything is pre-configured for MSTR Tutorial. OM_packages/ contains the bundle:

Package	Provides
tutorial_objetcs.mmp	Folder structure, MTDI cubes and MSTR application objects — deploy this first
PA_Objects.mmp	Only needed for REGAM regression testing; supplies the report used to replay user jobs from Platform Analytics

Prerequisites: access to MSTR Tutorial and Platform Analytics via Workstation and Library as a developer. A POC or development environment is recommended.

6. Configure the YAML files

Copy each *.example.* file in config/ to its real name and fill it in:

Example file	Becomes	Holds
user_d.example.yml	user_d.yml	MSTR connection and credentials
mstr_redis_y.example.yml	mstr_redis_y.yml	Redis connection
dans_migrations.example.yml	dans_migrations.yml	Migration settings
jupyter_objects_d.example.yml	jupyter_objects_d.yml	Object GUIDs per notebook
API_KEY.example.env	API_KEY.env	Perplexity API key

The real files are gitignored — keep it that way.

7. Run the notebooks in order

1. 00_setup
2. jup_load_rag_cubes
3. … then whichever notebook fits your task

Full walkthrough: docs/SETUP.md.

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Repository layout

Path	Contents
mstr_robotics/	The package: connection, objects, reports, migration, Redis, RAG, MCP servers, OSI exporter
notebooks/	Jupyter notebooks — the primary entry points
config/	*.example.* configuration templates
docs/	Setup and reference documentation
examples/	Sample data used by the notebooks
OM_packages/	MSTR Object Manager packages for Tutorial & PA
osi_templates/	Read-only OSI schema — the exporter reads it, nothing writes it
OSI_production/	Example OSI YAML export of a dashboard

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Community

• Free webinars every 2 to 4 weeks
• Contributions are very welcome — especially senior Python review
• Ideas on how to push (Micro-)Strategy community packages forward are welcome too
• Open comments appreciated: comment or DM

Need more hands-on help? Workshops (1, 3 or 5 days) and Strategy Python automation work are available — 1.500 € / day. Send a direct message if interested.

Questions or a failing OM package import: [e-mail]